Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

A Machine Learning Approach For Opinion Holder Extraction In Arabic Language

Opinion mining aims at extracting useful subjective information from reliable amounts of text. Opinion mining holder recognition is a task that has not been considered yet in Arabic Language. This task essentially requires deep understanding of clauses structures. Unfortunately, the lack of a robust, publicly available, Arabic parser further complicates the research. This paper presents a leading research for the opinion holder extraction in Arabic news independent from any lexical parsers. We investigate constructing a comprehensive feature set to compensate the lack of parsing structural outcomes. The proposed feature set is tuned from English previous works coupled with our proposed semantic field and named entities features. Our feature analysis is based on Conditional Random Fields (CRF) and semi-supervised pattern recognition techniques. Different research models are evaluated via cross-validation experiments achieving 54.03 F-measure. We publicly release our own research outcome corpus and lexicon for opinion mining community to encourage further research

A literature survey of methods for analysis of subjective language

Subjective language is used to express attitudes and opinions towards things, ideas and people. While content and topic centred natural language processing is now part of everyday life, analysis of subjective aspects of natural language have until recently been largely neglected by the research community. The explosive growth of personal blogs, consumer opinion sites and social network applications in the last years, have however created increased interest in subjective language analysis. This paper provides an overview of recent research conducted in the area

Survey on Evaluation Methods for Dialogue Systems

In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class

Product Question Answering in E-Commerce: A Survey

Product question answering (PQA), aiming to automatically provide instant responses to customer's questions in E-Commerce platforms, has drawn increasing attention in recent years. Compared with typical QA problems, PQA exhibits unique challenges such as the subjectivity and reliability of user-generated contents in E-commerce platforms. Therefore, various problem settings and novel methods have been proposed to capture these special characteristics. In this paper, we aim to systematically review existing research efforts on PQA. Specifically, we categorize PQA studies into four problem settings in terms of the form of provided answers. We analyze the pros and cons, as well as present existing datasets and evaluation protocols for each setting. We further summarize the most significant challenges that characterize PQA from general QA applications and discuss their corresponding solutions. Finally, we conclude this paper by providing the prospect on several future directions

Answering the Calls of "What's Next" and "Library Workers Cannot Live by Love Alone" through Certification and Salary Research

Members and staff of the American Library Association (ALA) worked diligently over more than a decade to develop a certification program for public library managers. Spurred by a long-standing trend in many other terminal-degree professions that have post-degree, voluntary certifications, the Certified Public Library Administrator Program was born. Legal authority recommended the establishment of a service organization, a 501(c)(6) to manage the program, which has become one of several programs that will be offered to library employees under the imprimatur of ALA. After the American Library Association???Allied Professional Association (ALA-APA) was instituted, advocacy for salary improvement initiatives was appended to the mission. One means of salary advocacy was to improve available data by expanding the scope and usefulness of the ALA Survey of Librarian Salaries, which resulted in the ALA-APA Salary Survey: Non-MLS???Public and Academic, conducted in 2006 and 2007 to collect salary data from more than sixty positions in the field that do not require a master's degree in Library Science. The experience of establishing two certification programs, the Certified Public Library Administrator Program (CPLA??) and the Library Support Staff Certification Program, has been a study in creating new national models of professional development. This article will also discuss the insights that have emerged from fulfilling elements of ALA strategic plans concerning the needs of support staff through certification and the salary survey.published or submitted for publicatio

Empirical Methodology for Crowdsourcing Ground Truth

The process of gathering ground truth data through human annotation is a major bottleneck in the use of information extraction methods for populating the Semantic Web. Crowdsourcing-based approaches are gaining popularity in the attempt to solve the issues related to volume of data and lack of annotators. Typically these practices use inter-annotator agreement as a measure of quality. However, in many domains, such as event detection, there is ambiguity in the data, as well as a multitude of perspectives of the information examples. We present an empirically derived methodology for efficiently gathering of ground truth data in a diverse set of use cases covering a variety of domains and annotation tasks. Central to our approach is the use of CrowdTruth metrics that capture inter-annotator disagreement. We show that measuring disagreement is essential for acquiring a high quality ground truth. We achieve this by comparing the quality of the data aggregated with CrowdTruth metrics with majority vote, over a set of diverse crowdsourcing tasks: Medical Relation Extraction, Twitter Event Identification, News Event Extraction and Sound Interpretation. We also show that an increased number of crowd workers leads to growth and stabilization in the quality of annotations, going against the usual practice of employing a small number of annotators.Comment: in publication at the Semantic Web Journa

Determination of competency framework for technical and vocational education and training (TVET) educators in Nigerian tertiary institutions

Lack of competent TVET Educators in Nigerian institutions has led to several problems such as low quality graduates and unemployment. Competency is a vital element for assessing the quality of technical and vocational education and training (TVET) Educators. Therefore, this research investigated the TVET Educators ’ perceptions on competency needs in Nigerian tertiary institutions based on Malaysian Human Resource Development Practitioners (MHRDP) competency model for workplace learning and performance (WLP). Apart from that, this study also aimed at investigating the perception differences on competency elements among difference TVET tertiary institutions in order to enhance their quality. The study was fully quantitative and 218 questionnaires were systematically distributed to the TVET educators from five tertiary institutions based on the stratified sampling technique. A total of 205 questionnaires were returned. Descriptive and inferential statistical methods such as mean, EFA and ANOVA were used to analyse the data. The research found that Nigerian TVET educators perceived all the competency elements (25 constituents) as important; 19 out 25 constituents of competency framework were significantly related to Nigerian tertiary institutions. The research findings also revealed that there was no statistically significant differences among the TVET educators perception on competency elements across different types of TVET tertiary institutions. The developed competency framework for Nigerian TVET tertiary institutions contributes originally to the body of knowledge. The research recommends that government and other relevant authorities should emphasize on the implementation of the framework to tertiary institutions in Nigeria. A similar research should be undertaken to extend the result to reflect other Non-TVET educators in Nigeria